Yeast Transcription Factor Research Articles

Transcription factor condensates, 3D clustering, and gene expression enhancement of the MET regulon.

Some transcription factors (TFs) can form liquid-liquid phase separated (LLPS) condensates. However, the functions of these TF condensates in 3-Dimentional (3D) genome organization and gene regulation remain elusive. In response to methionine (met) starvation, budding yeast TF Met4 and a few co-activators, including Met32, induce a set of genes involved in met biosynthesis. Here, we show that the endogenous Met4 and Met32 form co-localized puncta-like structures in yeast nuclei upon met depletion. Recombinant Met4 and Met32 form mixed droplets with LLPS properties in vitro. In relation to chromatin, Met4 puncta co-localize with target genes, and at least a subset of these target genes is clustered in 3D in a Met4-dependent manner. A MET3pr-GFP reporter inserted near several native Met4-binding sites becomes co-localized with Met4 puncta and displays enhanced transcriptional activity. A Met4 variant with a partial truncation of an intrinsically disordered region (IDR) shows less puncta formation, and this mutant selectively reduces the reporter activity near Met4-binding sites to the basal level. Overall, these results support a model where Met4 and co-activators form condensates to bring multiple target genes into a vicinity with higher local TF concentrations, which facilitates a strong response to methionine depletion.

Transcription factor condensates, 3D clustering, and gene expression enhancement of the MET regulon

Some transcription factors (TFs) can form liquid–liquid phase separated (LLPS) condensates. However, the functions of these TF condensates in 3-Dimentional (3D) genome organization and gene regulation remain elusive. In response to methionine (met) starvation, budding yeast TF Met4 and a few co-activators, including Met32, induce a set of genes involved in met biosynthesis. Here, we show that the endogenous Met4 and Met32 form co-localized puncta-like structures in yeast nuclei upon met depletion. Recombinant Met4 and Met32 form mixed droplets with LLPS properties in vitro. In relation to chromatin, Met4 puncta co-localize with target genes, and at least a subset of these target genes is clustered in 3D in a Met4-dependent manner. A MET3pr-GFP reporter inserted near several native Met4-binding sites becomes co-localized with Met4 puncta and displays enhanced transcriptional activity. A Met4 variant with a partial truncation of an intrinsically disordered region (IDR) shows less puncta formation, and this mutant selectively reduces the reporter activity near Met4-binding sites to the basal level. Overall, these results support a model where Met4 and co-activators form condensates to bring multiple target genes into a vicinity with higher local TF concentrations, which facilitates a strong response to methionine depletion.

Single-Neuron Labeling in Drosophila Using Multicolor FLP-Out.

Neurons exhibit some of the most striking examples of morphological diversity of any cell type. Thus, when studying neurons, the morphology of each neuron must be considered individually. However, neurons densely populate the central nervous system (CNS), making it difficult to ascertain fine morphological features due to a lack of spatial resolution. In Drosophila, this problem can be partially resolved by using driver lines that express the yeast transcription factor GAL4 in subsets of neurons. GAL4 can activate the expression of other introduced genetic elements such as genes for fluorescent proteins or other markers under the control of the GAL4 upstream activation sequences (UAS effectors). However, even highly specific GAL4 lines often label sets of potentially morphologically heterogeneous neurons. Here, we describe a protocol for using the multicolor flip-out (MCFO) technique in Drosophila melanogaster to stochastically label individual neurons within a GAL4 expression pattern. MCFO relies on the binary GAL4/UAS expression system in Drosophila but adds additional control for how densely the neurons within a GAL4 expression pattern are labeled via user-controlled heat shock. Specifically, three discrete UAS effector elements containing the sequences for unique epitope tags (FLAG, HA, and V5) linked to a gene for nonfluorescent GFP can be independently expressed under the control of GAL4 only when a transcriptional stop sequence in the UAS promoter sequence has been removed by heat shock-induced recombination. This effectively labels multiple individual neurons with either one or a combination of epitope tags that can be spectrally resolved with immunofluorescence. The MCFO technique is ideal for researchers who want to determine morphological features of CNS neurons in wild-type or mutant backgrounds.

GSM1 Requires Hap4 for Expression and Plays a Role in Gluconeogenesis and Utilization of Nonfermentable Carbon Sources.

Multiple transcription factors in the budding yeast Saccharomyces cerevisiae are required for the switch from fermentative growth to respiratory growth. The Hap2/3/4/5 complex is a transcriptional activator that binds to CCAAT sequence elements in the promoters of many genes involved in the tricarboxylic acid cycle and oxidative phosphorylation and activates gene expression. Adr1 and Cat8 are required to activate the expression of genes involved in the glyoxylate cycle, gluconeogenesis, and utilization of nonfermentable carbon sources. Here, we characterize the regulation and function of the zinc cluster transcription factor Gsm1 using Western blotting and lacZ reporter-gene analysis. GSM1 is subject to glucose repression, and it requires a CCAAT sequence element for Hap2/3/4/5-dependent expression under glucose-derepression conditions. Genome-wide CHIP analyses revealed many potential targets. We analyzed 29 of them and found that FBP1, LPX1, PCK1, SFC1, and YAT1 require both Gsm1 and Hap4 for optimal expression. FBP1, PCK1, SFC1, and YAT1 play important roles in gluconeogenesis and utilization of two-carbon compounds, and they are known to be regulated by Cat8. GSM1 overexpression in cat8Δ mutant cells increases the expression of these target genes and suppresses growth defects in cat8Δ mutants on lactate medium. We propose that Gsm1 and Cat8 have shared functions in gluconeogenesis and utilization of nonfermentable carbon sources and that Cat8 is the primary regulator.

Bayesian inference of sample-specific coexpression networks.

Gene regulatory networks (GRNs) are effective tools for inferring complex interactions between molecules that regulate biological processes and hence can provide insights into drivers of biological systems. Inferring coexpression networks is a critical element of GRN inference, as the correlation between expression patterns may indicate that genes are coregulated by common factors. However, methods that estimate coexpression networks generally derive an aggregate network representing the mean regulatory properties of the population and so fail to fully capture population heterogeneity. Bayesian optimized networks obtained by assimilating omic data (BONOBO) is a scalable Bayesian model for deriving individual sample-specific coexpression matrices that recognizes variations in molecular interactions across individuals. For each sample, BONOBO assumes a Gaussian distribution on the log-transformed centered gene expression and a conjugate prior distribution on the sample-specific coexpression matrix constructed from all other samples in the data. Combining the sample-specific gene coexpression with the prior distribution, BONOBO yields a closed-form solution for the posterior distribution of the sample-specific coexpression matrices, thus allowing the analysis of large data sets. We demonstrate BONOBO's utility in several contexts, including analyzing gene regulation in yeast transcription factor knockout studies, the prognostic significance of miRNA-mRNA interaction in human breast cancer subtypes, and sex differences in gene regulation within human thyroid tissue. We find that BONOBO outperforms other methods that have been used for sample-specific coexpression network inference and provides insight into individual differences in the drivers of biological processes.

Transcription Factor Condensates Mediate Clustering of MET Regulon and Enhancement in Gene Expression.

Some transcription factors (TFs) can form liquid-liquid phase separated (LLPS) condensates. However, the functions of these TF condensates in 3D genome organization and gene regulation remain elusive. In response to methionine (met) starvation, budding yeast TF Met4 and a few co-activators, including Met32, induce a set of genes involved in met biosynthesis. Here, we show that the endogenous Met4 and Met32 form co-localized puncta-like structures in yeast nuclei upon met depletion. Recombinant Met4 and Met32 form mixed droplets with LLPS properties in vitro. In relation to chromatin, Met4 puncta co-localize with target genes, and at least a subset of these target genes is clustered in 3D in a Met4-dependent manner. A MET3pr-GFP reporter inserted near several native Met4 binding sites becomes co-localized with Met4 puncta and displays enhanced transcriptional activity. A Met4 variant with a partial truncation of an intrinsically disordered region (IDR) shows less puncta formation, and this mutant selectively reduces the reporter activity near Met4 binding sites to the basal level. Overall, these results support a model where Met4 and co-activators form condensates to bring multiple target genes into a vicinity with higher local TF concentrations, which facilitates a strong response to methionine depletion.

Flexible modeling of regulatory networks improves transcription factor activity estimation

Transcriptional regulation plays a crucial role in determining cell fate and disease, yet inferring the key regulators from gene expression data remains a significant challenge. Existing methods for estimating transcription factor (TF) activity often rely on static TF-gene interaction databases and cannot adapt to changes in regulatory mechanisms across different cell types and disease conditions. Here, we present a new algorithm - Transcriptional Inference using Gene Expression and Regulatory data (TIGER) - that overcomes these limitations by flexibly modeling activation and inhibition events, up-weighting essential edges, shrinking irrelevant edges towards zero through a sparse Bayesian prior, and simultaneously estimating both TF activity levels and changes in the underlying regulatory network. When applied to yeast and cancer TF knock-out datasets, TIGER outperforms comparable methods in terms of prediction accuracy. Moreover, our application of TIGER to tissue- and cell-type-specific RNA-seq data demonstrates its ability to uncover differences in regulatory mechanisms. Collectively, our findings highlight the utility of modeling context-specific regulation when inferring transcription factor activities.

Dynamic Multiplexed Control and Modeling of Optogenetic Systems Using the High-Throughput Optogenetic Platform, Lustro.

The ability to control cellular processes using optogenetics is inducer-limited, with most optogenetic systems responding to blue light. To address this limitation, we leverage an integrated framework combining Lustro, a powerful high-throughput optogenetics platform, and machine learning tools to enable multiplexed control over blue light-sensitive optogenetic systems. Specifically, we identify light induction conditions for sequential activation as well as preferential activation and switching between pairs of light-sensitive split transcription factors in the budding yeast, Saccharomyces cerevisiae. We use the high-throughput data generated from Lustro to build a Bayesian optimization framework that incorporates data-driven learning, uncertainty quantification, and experimental design to enable the prediction of system behavior and the identification of optimal conditions for multiplexed control. This work lays the foundation for designing more advanced synthetic biological circuits incorporating optogenetics, where multiple circuit components can be controlled using designer light induction programs, with broad implications for biotechnology and bioengineering.

The intestinal stem cell/enteroblast-GAL4 driver, escargot-GAL4, also manipulates gene expression in the juvenile hormone-synthesizing organ of Drosophila melanogaster

Intestinal stem cells (ISCs) of the fruit fly, Drosophila melanogaster, offer an excellent genetic model to explore homeostatic roles of ISCs in animal physiology. Among available genetic tools, the escargot (esg)-GAL4 driver, expressing the yeast transcription factor gene, GAL4, under control of the esg gene promoter, has contributed significantly to ISC studies. This driver facilitates activation of genes of interest in proximity to a GAL4-binding element, Upstream Activating Sequence, in ISCs and progenitor enteroblasts (EBs). While esg-GAL4 has been considered an ISC/EB-specific driver, recent studies have shown that esg-GAL4 is also active in other tissues, such as neurons and ovaries. Therefore, the ISC/EB specificity of esg-GAL4 is questionable. In this study, we reveal esg-GAL4 expression in the corpus allatum (CA), responsible for juvenile hormone (JH) production. When driving the oncogenic gene, RasV12, esg-GAL4 induces overgrowth in ISCs/EBs as reported, but also increases CA cell number and size. Consistent with this observation, animals alter expression of JH-response genes. Our data show that esg-GAL4-driven gene manipulation can systemically influence JH-mediated animal physiology, arguing for cautious use of esg-GAL4 as a “specific” ISC/EB driver to examine ISC/EB-mediated animal physiology.

Control of meiotic entry by dual inhibition of a key mitotic transcription factor

The mitosis to meiosis transition requires dynamic changes in gene expression, but whether and how the mitotic transcriptional machinery is regulated during this transition is unknown. In budding yeast, SBF and MBF transcription factors initiate the mitotic gene expression program. Here, we report two mechanisms that work together to restrict SBF activity during meiotic entry: repression of the SBF-specific Swi4 subunit through LUTI-based regulation and inhibition of SBF by Whi5, a functional homolog of the Rb tumor suppressor. We find that untimely SBF activation causes downregulation of early meiotic genes and delays meiotic entry. These defects are largely driven by the SBF-target G1 cyclins, which block the interaction between the central meiotic regulator Ime1 and its cofactor Ume6. Our study provides insight into the role of SWI4LUTI in establishing the meiotic transcriptional program and demonstrates how the LUTI-based regulation is integrated into a larger regulatory network to ensure timely SBF activity.

Control of meiotic entry by dual inhibition of a key mitotic transcription factor.

The mitosis to meiosis transition requires dynamic changes in gene expression, but whether and how the mitotic transcriptional machinery is regulated during this transition is unknown. In budding yeast, SBF and MBF transcription factors initiate the mitotic gene expression program. Here, we report two mechanisms that work together to restrict SBF activity during meiotic entry: repression of the SBF-specific Swi4 subunit through LUTI-based regulation and inhibition of SBF by Whi5, a functional homolog of the Rb tumor suppressor. We find that untimely SBF activation causes downregulation of early meiotic genes and delays meiotic entry. These defects are largely driven by the SBF-target G1 cyclins, which block the interaction between the central meiotic regulator Ime1 and its cofactor Ume6. Our study provides insight into the role of SWI4LUTI in establishing the meiotic transcriptional program and demonstrates how the LUTI-based regulation is integrated into a larger regulatory network to ensure timely SBF activity.

Transcription factor exchange enables prolonged transcriptional bursts

Single-molecule imaging inside living cells has revealed that transcription factors (TFs) bind to DNA transiently, but a long-standing question is how this transient binding is related to transcription activation. Here, we devised a microscopy method to simultaneously measure transient TF binding at a single locus and the effect of these binding events on transcription. We show that DNA binding of the yeast TF Gal4 activates transcription of a target gene within a few seconds, with at least ∼20% efficiency and with a high initiation rate of ∼1 RNA/s. Gal4 DNA dissociation decreases transcription rapidly. Moreover, at a gene with multiple binding sites, individual Gal4 molecules only rarely stay bound throughout the entire burst but instead frequently exchange during a burst to increase the transcriptional burst duration. Our results suggest a mechanism for enhancer regulation in more complex eukaryotes, where TF cooperativity and exchange enable robust and responsive transcription regulation.

A multilayer microfluidic system for studies of the dynamic responses of cellular proteins to oxygen switches at the single-cell level.

Oxygen levels vary in the environment. Oxygen availability has a major effect on almost all organisms, and oxygen is far more than a substrate for energy production. However, less is known about related biological processes under hypoxic conditions and about the adaptations to changing oxygen concentrations. The yeast Saccharomyces cerevisiae can adapt its metabolism for growth under different oxygen concentrations and can grow even under anaerobic conditions. Therefore, we developed a microfluidic device that can generate serial, accurately controlled oxygen concentrations for single-cell studies of multiple yeast strains. This device can construct a broad range of oxygen concentrations, [O2] through on-chip gas-mixing channels from two gases fed to the inlets. Gas diffusion through thin polydimethylsiloxane (PDMS) can lead to the equilibration of [O2] in the medium in the cell culture layer under gas cover regions within 2min. Here, we established six different and stable [O2] varying between ~0.1 and 20.9% in the corresponding layers of the device designed for multiple parallel single-cell culture of four different yeast strains. Using this device, the dynamic responses of different yeast transcription factors and metabolism-related proteins were studied when the [O2] decreased from 20.9% to serial hypoxic concentrations. We showed that different hypoxic conditions induced varying degrees of transcription factor responses and changes in respiratory metabolism levels. This device can also be used in studies of the aging and physiology of yeast under different oxygen conditions and can provide new insights into the relationship between oxygen and organisms. Integration, innovation and insight: Most living cells are sensitive to the oxygen concentration because they depend on oxygen for survival and proper cellular functions. Here, a composite microfluidic device was designed for yeast single-cell studies at a series of accurately controlled oxygen concentrations. Using this device, we studied the dynamic responses of various transcription factors and proteins to changes in the oxygen concentration. This study is the first to examine protein dynamics and temporal behaviors under different hypoxic conditions at the single yeast cell level, which may provide insights into the processes involved in yeast and even mammalian cells. This device also provides a base model that can be extended to oxygen-related biology and can acquire more information about the complex networks of organisms.

ChEC-seq2: an improved chromatin endogenous cleavage sequencing method and bioinformatic analysis pipeline for mapping in vivo protein-DNA interactions.

Defining the in vivo DNA binding specificity of transcription factors (TFs) has relied nearly exclusively on chromatin immunoprecipitation (ChIP). While ChIP reveals TF binding patterns, its resolution is low. Higher resolution methods employing nucleases such as ChIP-exo, chromatin endogenous cleavage (ChEC-seq) and CUT&RUN resolve both TF occupancy and binding site protection. ChEC-seq, in which an endogenous TF is fused to micrococcal nuclease, requires neither fixation nor antibodies. However, the specificity of DNA cleavage during ChEC has been suggested to be lower than the specificity of the peaks identified by ChIP or ChIP-exo, perhaps reflecting non-specific binding of transcription factors to DNA. We have simplified the ChEC-seq protocol to minimize nuclease digestion while increasing the yield of cleaved DNA. ChEC-seq2 cleavage patterns were highly reproducible between replicates and with published ChEC-seq data. Combined with DoubleChEC, a new bioinformatic pipeline that removes non-specific cleavage sites, ChEC-seq2 identified high-confidence cleavage sites for three different yeast TFs that are strongly enriched for their known binding sites and adjacent to known target genes.

Intrinsically disordered regions of the Msn2 transcription factor encode multiple functions using interwoven sequence grammars.

Intrinsically disordered regions (IDRs) are abundant in eukaryotic proteins, but their sequence-function relationship remains poorly understood. IDRs of transcription factors (TFs) can direct promoter selection and recruit coactivators, as shown for the budding yeast TF Msn2. To examine how IDRs encode both these functions, we compared genomic binding specificity, coactivator recruitment, and gene induction amongst a large set of designed Msn2-IDR mutants. We find that both functions depend on multiple regions across the>600AA IDR. Yet, transcription activity was readily disrupted by mutations that showed no effect on the Msn2 binding specificity. Our data attribute this differential sensitivity to the integration of a relaxed, composition-based code directing binding specificity with a more stringent, motif-based code controlling the recruitment of coactivators and transcription activity. Therefore, Msn2 utilizes interwoven sequence grammars for encoding multiple functions, suggesting a new IDR design paradigm of potentially general use.

The architecture of binding cooperativity between densely bound transcription factors.

The binding of transcription factors (TFs) along genomes is restricted to a subset of sites containing their preferred motifs. TF-binding specificity is often attributed to the co-binding of interacting TFs; however, apart from specific examples, this model remains untested. Here, we define dependencies among budding yeast TFs that localize to overlapping promoters by profiling the genome-wide consequences of co-depleting multiple TFs. We describe unidirectional interactions, revealing Msn2 as a central factor allowing TF binding at its target promoters. By contrast, no case of mutual cooperation was observed. Particularly, Msn2 retained binding at its preferred promoters upon co-depletion of fourteen similarly bound TFs. Overall, the consequences of TF co-depletions were moderate, limited to a subset of promoters, and failed to explain the role of regions outside the DNA-binding domain in directing TF-binding preferences. Our results call for re-evaluating the role of cooperative interactions in directing TF-binding preferences.

Yeast transcription factor Msn2 binds to G4 DNA.

Sequences capable of forming quadruplex or G4 DNA are prevalent in the promoter regions. The transformation from canonical to non-canonical secondary structure apparently regulates transcription of a number of human genes. In the budding yeast Saccharomyces cerevisiae, we identified 37 genes with a G4 motif in the promoters including 20 genes that contain stress response element (STRE) overlapping a G4 motif. STRE is the binding site of stress response regulators Msn2 and Msn4, transcription factors belonging to the C2H2 zinc-finger protein family. We show here that Msn2 binds directly to the G4 DNA structure through its zinc-finger domain with a dissociation constant similar to that of STRE-binding and that, in a stress condition, Msn2 is enriched at G4 DNA-forming loci in the yeast genome. For a large fraction of genes with G4/STRE-containing promoters, treating with G4-ligands led to significant elevations in transcription levels. Such transcriptional elevation was greatly diminished in a msn2Δ msn4Δ background and was partly muted when the G4 motif was disrupted. Taken together, our data suggest that G4 DNA could be an alternative binding site of Msn2 in addition to STRE, and that G4 DNA formation could be an important element of transcriptional regulation in yeast.

The yeast transcription factor Stb5 acts as a negative regulator of autophagy by modulating cellular metabolism

ABSTRACT Macroautophagy/autophagy is a highly conserved pathway of cellular degradation and recycling that maintains cell health during homeostatic conditions and facilitates survival during stress. Aberrant cellular autophagy contributes to the pathogenesis of human diseases such as cancer, neurodegeneration, and cardiovascular, metabolic and lysosomal storage disorders. Despite decades of research, there remain unanswered questions as to how autophagy modulates cellular metabolism, and, conversely, how cellular metabolism affects autophagy activity. Here, we have identified the yeast metabolic transcription factor Stb5 as a negative regulator of autophagy. Chromosomal deletion of STB5 in the yeast Saccharomyces cerevisiae enhances autophagy. Loss of Stb5 results in the upregulation of select autophagy-related (ATG) transcripts under nutrient-replete conditions; however, the Stb5-mediated impact on autophagy occurs primarily through its effect on genes involved in NADPH production and the pentose phosphate pathway. This work provides insight into the intersection of Stb5 as a transcription factor that regulates both cellular metabolic responses and autophagy activity. Abbreviations: bp, base pairs; ChIP, chromatin immunoprecipitation; G6PD, glucose-6-phosphate dehydrogenase; GFP, green fluorescent protein; IDR, intrinsically disordered region; NAD, nicotinamide adenine dinucleotide; NADP+, nicotinamide adenine dinucleotide phosphate; NADPH, nicotinamide adenine dinucleotide phosphate (reduced); ORF, open reading frame; PA, protein A; PCR, polymerase chain reaction; PE, phosphatidylethanolamine; PPP, pentose phosphate pathway; prApe1, precursor aminopeptidase I; ROS, reactive oxygen species; RT-qPCR, real-time quantitative PCR; SD, standard deviation; TF, transcription factor; TOR, target of rapamycin; WT, wild-type.

Zinc cluster transcription factors frequently activate target genes using a non-canonical half-site binding mode.

Gene expression changes are orchestrated by transcription factors (TFs), which bind to DNA to regulate gene expression. It remains surprisingly difficult to predict basic features of the transcriptional process, including in vivo TF occupancy. Existing thermodynamic models of TF function are often not concordant with experimental measurements, suggesting undiscovered biology. Here, we analyzed one of the most well-studied TFs, the yeast zinc cluster Gal4, constructed a Shea-Ackers thermodynamic model to describe its binding, and compared the results of this model to experimentally measured Gal4p binding in vivo. We found that at many promoters, the model predicted no Gal4p binding, yet substantial binding was observed. These outlier promoters lacked canonical binding motifs, and subsequent investigation revealed Gal4p binds unexpectedly to DNA sequences with high densities of its half site (CGG). We confirmed this novel mode of binding through multiple experimental and computational paradigms; we also found most other zinc cluster TFs we tested frequently utilize this binding mode, at 27% of their targets on average. Together, these results demonstrate a novel mode of binding where zinc clusters, the largest class of TFs in yeast, bind DNA sequences with high densities of half sites.

Complementary strategies for directing in vivo transcription factor binding through DNA binding domains and intrinsically disordered regions

DNA binding domains (DBDs) of transcription factors (TFs) recognize DNA sequence motifs that are highly abundant in genomes. Within cells, TFs bind a subset of motif-containing sites as directed by either their DBDs or DBD-external (nonDBD) sequences. To define the relative roles of DBDs and nonDBDs in directing binding preferences, we compared the genome-wide binding of 48 (∼30%) budding yeast TFs with their DBD-only, nonDBD-truncated, and nonDBD-only mutants. With a few exceptions, binding locations differed between DBDs and TFs, resulting from the cumulative action of multiple determinants mapped mostly to disordered nonDBD regions. Furthermore, TFs' preferences for promoters of the fuzzy nucleosome architecture were lost in DBD-only mutants, whose binding spread across promoters, implicating nonDBDs' preferences in this hallmark of budding yeast regulatory design. We conclude that DBDs and nonDBDs employ complementary DNA-targeting strategies, whose balance defines TF binding specificity along genomes.